Immune checkpoint inhibitor rechallenge in patients who previously experienced immune-related inflammatory arthritis: a multicentre observational study
Bibliographic record
Abstract
OBJECTIVE: Another course of immune checkpoint inhibitors (ICIs) is often considered in patients with cancer progression and previous immune-related adverse events, including inflammatory arthritis (ICI-IA), but there are limited data regarding safety of ICI rechallenge in this setting. We aimed to assess the rate and clinical features associated with ICI-IA flare/recurrence on ICI rechallenge. METHODS: We conducted a multicentre observational study including cancer patients with ICI-IA who started a second course of ICI more than 3 months after ICI discontinuation in four French university hospitals. Primary outcome was the frequency of ICI flare/recurrence after ICI rechallenge. RESULTS: Twenty-three patients were included. At the time of ICI rechallenge, 18 patients reported no symptoms of ICI-IA (78%) and 5 had grade 1 (22%), 11 patients (48%) were not receiving any ICI-IA treatment, 11 (48%) were still on prednisone, 2 (9%) were on conventional synthetic disease-modifying antirheumatic drugs and 1 (4%) on anti-IL-6. ICI-IA flare/recurrence occurred in 12 patients (52%) with a median time of 1 month after ICI rechallenge. ICI-IA phenotype, disease activity and ICI-IA treatment at the time of ICI rechallenge did not differ according to ICI-IA flare/recurrence status. CONCLUSION: In this first observational study of ICI-IA patients rechallenged with ICI, about half of the patients experienced ICI-IA flare/recurrence with a similar phenotype but occurring earlier than the initial ICI-IA, warranting close monitoring during the first month of retreatment. Risk of flare did not differ according to baseline immunosuppressive treatment at the time of rechallenge.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".